Experiential learning through STEM: Recent initiatives in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper reviews recent educational initiatives in science, technology, engineering and math (STEM) education in the United States, asking to what extent experiential learning methods are being incorporated into STEM education. We draw on a combination of qualitative and quantitative evidence. The quantitative evidence is from an analysis of the proposal abstracts for all 11,406 of the STEM education and workforce development‐related projects funded by NSF grants from the end of 2018 to the beginning of 2022. The qualitative portion of the paper analyzes results from a number of scholarly studies of local initiatives from the last 10 years drawn from a range of published and conference papers, reports and media stories, and project websites, drawn from education research databases, secondary literature, and websites of specific organizations. We seek to classify and describe patterns observed among the projects examined, identifying common patterns and combinations of features. We believe that the paper represents the first comprehensive study of efforts to employ experiential learning methods in STEM education to link formal and informal aspects of learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it